library("tidyverse")
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## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(maps)
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## Attaching package: 'maps'
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## map
library(mapdata)
library(lubridate)
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## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
library(viridis)
## Loading required package: viridisLite
library(wesanderson)
library(plotly)
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## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
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## filter
## The following object is masked from 'package:graphics':
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## layout
library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
Exercises
Exercise 1
### For the above graph “World COVID-19 Confirmed case” summarize the counts for each Country on the graph and update the graph to 9/26/2020. You may need to adjust the size of the points.
### load data from 9.26.20
daily_report_ex1 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_")
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
### we adjust the confirmed cases by dividing by 1 million
ggplot(daily_report_ex1, aes(x = Long, y = Lat, size = Confirmed/1000000)) +
borders("world", colour = NA, fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='blue', fill='red', alpha = 0.5) +
labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
size="Cases (x1000000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)
## Warning: Removed 81 rows containing missing values (geom_point).
Exercise 2
### Update Anisa Dhana’s graph layout of the US to 9/26/2020. You may need to adjust the size of the points.
### load us data from 9.26.20
daily_report_ex2 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Country_Region == "US") %>%
filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
"Puerto Rico","Northern Mariana Islands",
"Virgin Islands", "Recovered", "Guam", "Grand Princess",
"District of Columbia", "Diamond Princess")) %>%
filter(Lat > 0)
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
### change range= c(1,3) to visualize better
mybreaks <- c(100, 1000, 10000, 100000, 1000000)
ggplot(daily_report_ex2, aes(x = Long, y = Lat, size = Confirmed)) +
borders("state", colour = "white", fill = "grey90") +
geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
scale_size_continuous(name="Cases", trans="log", range=c(1,3),
breaks=mybreaks, labels = c("100-999",
"1,000-9,999", "10,000-99,999", "100,000-999,999", "1,000,000-9,999,999")) +
scale_color_viridis_c(option="viridis",name="Cases",
trans="log", breaks=mybreaks, labels = c("100-999",
"1,000-9,999", "10,000-99,999", "100,000-999,999", "1,000,000-9,999,999")) +
# Cleaning up the graph
theme_void() +
guides( colour = guide_legend()) +
labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US - 9/26/20") +
theme(
legend.position = "bottom",
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff", color = NA),
legend.background = element_rect(fill = "#ffffff", color = NA)
) +
coord_fixed(ratio=1.5)
## Warning: Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis
## Warning in sqrt(x): NaNs produced
## Warning: Removed 6 rows containing missing values (geom_point).
Exercise 3
### Update the above graph “Number of Confirmed Cases by US County” to 9/26/2020 and use a different color scheme or theme
# Get and format the covid report data
report_09_26_2020 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
unite(Key, Admin2, Province_State, sep = ".") %>%
group_by(Key) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Key = tolower(Key))
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# dim(report_09_26_2020)
# get and format the map data
us_ex3 <- map_data("state")
counties_ex3 <- map_data("county") %>%
unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join_ex3 <- left_join(counties_ex3, report_09_26_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
# plot new data
ggplot(data = us_ex3, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
borders("state", colour = "black") +
geom_polygon(data = state_join_ex3, aes(fill = Confirmed)) +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "Set2"),
breaks = c(1, 10, 100, 1000, 10000, 100000),
trans = "log10", na.value = "White") +
ggtitle("Number of Confirmed Cases by US County") +
theme_bw()
## Warning: Transformation introduced infinite values in discrete y-axis
Exercise 4
### Make an interactive plot using a state of your choosing using a theme different from used in the above examples.
### Use Florida data
daily_report_ex4 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Province_State == "Florida") %>%
group_by(Admin2) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Admin2 = tolower(Admin2))
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
us_ex4 <- map_data("state")
fl_us <- subset(us_ex4, region == "florida")
counties <- map_data("county")
fl_county <- subset(counties, region == "florida")
state_join_ex4 <- left_join(fl_county, daily_report_ex4, by = c("subregion" = "Admin2"))
# plot state map
ggplotly(
ggplot(data = fl_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join_ex4, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colours =
wes_palette("FantasticFox1", 100, type = "continuous")) +
ggtitle("COVID-19 Cases in FL - 9/26/20") +
# Cleaning up the graph
labs(x=NULL, y=NULL) +
theme(panel.border = element_blank()) +
theme(panel.background = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.text = element_blank())
)
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Exercise 5
### Create a report with static maps and interactive graphs that is meant to be read by others (e.g. your friends and family). Hide warnings, messages and even the code you used so that it is readable. Included references. Link to the Lab 10 report from your Github site. Submit the link to Moodle. animations that is meant to be read by others (e.g. your friends and family). Hide warnings, messages and even the code you used so that it is readable. Included references. Link to the Lab 6 report from your Github site. Submit the link to Moodle.
# Please see : (labreport6_lorenestasiuk.html)